Comparing Color Descriptors between Image Segments for Saliency Detection

Anurag Singh, Henry Chu, Michael Pratt

2016

Abstract

Detecting salient regions in an image or video frame is an important step in early vision and image understanding. We present a visual saliency detection method by measuring the difference in color content of an image segment with that of its neighbors. We represent each segment with richer color descriptors in the form of a regional dominant color descriptor. The color difference between a pair of neighbors is found using the Earth Mover’s Distance. The cost of moving color descriptors between neighboring segments robustly captures the difference between neighboring segments. We evaluate our method on standard datasets and compare it with other state-of-the-art methods to demonstrate that it has better true positive rate at a fixed false positive rate in detecting salient pixels relative to the ground truth. The proposed method uses local cues without being an edge highlighter, a common problem of local contrast-based methods.

References

  1. Achanta, R., Hemami, S., Estrada, F., and Süsstrunk, S. (2009). Frequency-tuned salient region detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1597-1604.
  2. Alexe, B., Deselaers, T., and Ferrari, V. (2012). Measuring the objectness of image windows. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11):2189-2202.
  3. Alpert, S., Galun, M., Basri, R., and Brandt, A. (2007). Image segmentation by probabilistic bottom-up aggregation and cue integration. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1-8.
  4. Bagon, S., Boiman, O., and Irani, M. (2008). What is a good image segment? a unified approach to segment extraction. In Proceedings of the 10th European Conference on Computer Vision: Part IV, pages 30-44.
  5. Ben-Av, M., Sagi, D., and Braun, J. (1992). Visual attention and perceptual grouping. Perception and Psychophysics, 52(3):277-294.
  6. Borji, A. and Itti, L. (2013). State-of-the-art in visual attention modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(1):185-207.
  7. Cheng, M.-M., Zhang, G.-X., Mitra, N. J., Huang, X., and Hu, S.-M. (2011). Global contrast based salient region detection. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 7811, pages 409-416.
  8. Engel, S., Zhang, X., and Wandell, B. (1997). Colour tuning in human visual cortex measured with functional magnetic resonance imaging. Nature, 388(6637):68- 71.
  9. Felzenszwalb, P. and Huttenlocher, D. (2004). Efficient graph-based image segmentation. International Journal Computer Vision, 59(2):167-181.
  10. Goferman, S., Zelnik-Manor, L., and Tal, A. (2010). Context-aware saliency detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2376-2383.
  11. Harel, J., Koch, C., and Perona, P. (2006). Graph-based visual saliency. In Advances in Neural Information Processing Systems, pages 545-552. MIT Press.
  12. Itti, L., Koch, C., and Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11):1254-1259.
  13. Li, J., Levine, M., An, X., Xu, X., and He, H. (2013). Visual saliency based on scale-space analysis in the frequency domain. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(4):996-1010.
  14. Li, Y., Hou, X., Koch, C., Rehg, J., and Yuille, A. (2014). The secrets of salient object segmentation. In IEEE Conference on Computer Vision and Pattern Recognition, pages 4321-4328.
  15. Lin, Y., Tang, Y., Fang, B., Shang, Z., Huang, Y., and Wang, S. (2013). A visual-attention model using earth mover's distance-based saliency measurement and nonlinear feature combination. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(2):314-328.
  16. Liu, T., Sun, J., Zheng, N.-N., Tang, X., and Shum, H.-Y. (2007). Learning to detect a salient object. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1-8.
  17. Ma, Y.-F. and Zhang, H.-J. (2003). Contrast-based image attention analysis by using fuzzy growing. In ACM International Conference on Multimedia, pages 374- 381.
  18. Manjunath, B. S., Ohm, J.-R., Vasudevan, V. V., and Yamada, A. (2001). Color and texture descriptors. IEEE Transactions on Circuits and Systems for Video Technology, 11(6):703-715.
  19. Perazzi, F., Krahenbuhl, P., Pritch, Y., and Hornung, A. (2012). Saliency filters: Contrast based filtering for salient region detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 733-740.
  20. Rubner, Y., Tomasi, C., and Guibas, L. (2000a). The earth mover's distance as a metric for image retrieval. International Journal of Computer Vision, 40(2):99-121.
  21. Rubner, Y., Tomasi, C., and Guibas, L. J. (2000b). The earth mover's distance as a metric for image retrieval. International Journal of Computer Vision, 40(2):99- 121.
  22. Singh, A., Chu, C., and Pratt, M. A. (2014). Multiresolution superpixels for visual saliency detection. In IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing, pages 1-8.
  23. Sun, J. and Ling, H. (2013). Scale and object aware image thumbnailing. International Journal of Computer Vision, 104(2):135-153.
  24. Treisman, A. (1982). Perceptual grouping and attention in visual search for features and for objects. Journal of Experimental Psychology: Human Perception and Performance, 8(2):194.
  25. Yan, Q., Xu, L., Shi, J., and Jia, J. (2013). Hierarchical saliency detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1155-1162.
  26. Yang, N.-C., Chang, W.-H., Kuo, C.-M., and Li, T.-H. (2008). A fast mpeg-7 dominant color extraction with new similarity measure for image retrieval. Journal of Visual Communication and Image Representation, 19(2):92-105.
  27. Zhai, Y. and Shah, M. (2006). Visual attention detection in video sequences using spatiotemporal cues. In ACM International Conference on Multimedia, pages 815- 824.
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Paper Citation


in Harvard Style

Singh A., Chu H. and Pratt M. (2016). Comparing Color Descriptors between Image Segments for Saliency Detection . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 558-565. DOI: 10.5220/0005667705580565


in Bibtex Style

@conference{icpram16,
author={Anurag Singh and Henry Chu and Michael Pratt},
title={Comparing Color Descriptors between Image Segments for Saliency Detection},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={558-565},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005667705580565},
isbn={978-989-758-173-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Comparing Color Descriptors between Image Segments for Saliency Detection
SN - 978-989-758-173-1
AU - Singh A.
AU - Chu H.
AU - Pratt M.
PY - 2016
SP - 558
EP - 565
DO - 10.5220/0005667705580565